Mapping Institutional Innovation in multi-level governance policy contexts: the case of Climate Change Adaptation in Portugal

09:00 Thursday 30 May

OC216

Room S1

 

Joao Mourato (Portugal) 1; Fronika De Wit (Portugal) 1; Alexandra Bussler (Portugal) 1; Joao Ferrao (Portugal) 1

1 - ICS - Institute of Social Sciences - University of Lisbon

Local adaptation to climate change has evolved into a booming policy field. Recent comparative overviews in Europe are witnessing an increasing production of local or urban climate adaptation plans or strategies. This comes as no surprise as the acknowledgment that climate change poses significant risks for cities and communities is steadily growing. Consequently, governments have raced to set in place adaptation instruments and policies to try to reduce the vulnerability to these risks and help mitigate their negative impacts. In light of a future increasing need for local-level led climate change adaptation (CCA), it is of utmost importance to understand the dynamics of such policy-generating processes in order to render them more effective and maximise their positive outcomes for adaptation.

This paper builds on the narrative of a unique process of local CCA policy design and institutional capacity building, the case of the Portuguese research-led ClimAdaPT.Local project.

With a scale and scope of unprecedented nature, it brought together researchers, policy and decision-makers and the wider public in an experimental participative co-designing process. Now, as its policy outcomes undergo implementation, it is time to critically review both project and process, to better grasp its impacts at the multiple policy (i.e. local, regional, and national) and research (i.e. methodological and conceptual/interdisciplinarity ) levels.

This paper’s analysis aims to look beyond immediate outcomes and discuss the wider impact of these CCA policy design and implementation processes, i.e. their contribution as potential promoters of spatial justice, democratization, social and institutional learning. By doing so, we aim to strengthen the original analytical matrix, while at the same time exposing what we believe are fundamental effects of local adaptation policy processes that often go unnoticed.